Performance Engineering

Software Performance Engineering: Biggest Challenges (and Solutions) in 2024

Nacho Rego

June 3, 2024

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As we move into the holiday season of 2024, companies are gearing up to provide the user experience that clients and customers have learned to expect.

Performance engineering ensures that websites, applications, and systems run seamlessly during peak holiday periods.

In this post, we begin our series exploring the biggest software performance engineering challenges in the coming year and how our framework solves them.

Why Is Performance Engineering Important (for Holiday Readiness)?

The holiday season is a time when your business experiences a significant increase in traffic, and any issues with performance can lead to a loss of revenue, customer loyalty, and brand reputation.

Unfortunately, most organizations (even those with large budgets and employee counts into the tens of thousands) lack a formal team and are faced with a knowledge gap when it comes to proper performance testing.

What Are the Biggest Web Performance Engineering Challenges in 2024?

Below are the 4 top software performance engineering challenges we anticipate for the remainder of 2024.

1. Complexity and Scale of Systems

As technology continues to evolve so does the complexity of the systems we use.

With an increasing number of components, devices, and connections involved in modern systems, ensuring optimal performance becomes a daunting task.

The performance of these systems can be impacted by a range of factors, including hardware configurations, network latency, and software bugs.

For example, one of our clients required an unprecedented load test. Normally, we’d consult with the client to find the right tool for the job.

However, no market tool existed to handle the size and complexity of what they required.

At Perform, we built a home-grown tool for the (huge) company. One that could handle not only the immense load size but the various models and protocols like RPC (Remote Procedure Calls) and Google Protobuf their systems used.

We also worked with them to develop a framework for testing. Everything was off-market, which meant that training resources didn’t exist.

Normally, there are market tools available. In those cases, we offer to consult with clients to help them choose the right (and most cost-effective) tool for the job.

2. Artificial Intelligence and Machine Learning

AI and ML algorithms require large amounts of data, which puts a significant strain on system resources. The data itself must be validated in terms of “cleanliness,” and accuracy. Unpredictable data (that’s often difficult to debug) leads to performance issues.

To deal with this, Performhas partnered with leading web performance testing companies to identify and diagnose performance issues, as well as optimize the performance of AI and ML systems to ensure they’re running at peak performance.

With their tools, we can give our clients a unique advantage when it comes to validating the integrity and amount of data being transferred from various sources.

By starting with data validation, we can then identify and diagnose performance issues, as well as optimize the performance of AI and ML systems during peak times.

3. Containers and Microservices

These two services go hand-in-hand and continue to grow in popularity.

The challenge they pose to software performance engineering is due to the complexity of distributed systems.

Additionally, the dynamic nature of containerized environments makes it difficult to pinpoint performance issues and trace them back to specific components.


Microservices are an architectural approach. They break down applications into smaller, independent components that can be developed, deployed, and scaled independently. Once broken down, each component has its own set of responsibilities.

Microservices are typically dependent on multiple third-party services. These services talk to each other, clients, customers, etc.

So, first, we tend to recommend excluding external services from being tested if they don’t need to be.

When testing the microservices themselves we focus on optimization. That means figuring out the load volume it can handle and then adding complexities like other systems.

In some cases, clients might want to integrate third-party vendors and run a complete performance test.

When dealing with microservices that don’t like to be performance tested (like payment gateways), virtualization services come into play.


Containers are a lightweight way of packaging software with all its dependencies. They enable your applications to run consistently across different environments.

Perform has developed a model for performance testing containers and microservices.

We optimize both the resources for each container and the overall machine. Next, we divide and distribute the resources among each container in a way where they can handle their own load.

Testing and autoscaling for each container cluster are then needed. For that, we develop autoscaling policies based on the system’s needs and capabilities. These policies govern how fast clusters will autoscale up or down.

4. Cloud-Based Applications​

Cloud-based applications provide a flexible and scalable way to deliver applications. They enable your users to access resources on-demand, without the need for extensive hardware or software infrastructure.

The Perform has extensive experience working with cloud-based applications on various cloud platforms. Even though the infrastructure fundamentals remain the same, the scale and complexity multiply with the cloud.

The biggest problem we see is that companies regard cloud-based applications as a quick and easy solution for deploying large amounts of resources at any time. The result is that most organizations have more resources than necessary deployed.

For one of our clients, the cost of deploying these resources was exceptionally high.

We changed the instance types and sizes to more appropriate ones. Then we optimized their hardware resource use for each instance. Last, we updated their autoscaling policies and identified gaps in their monitoring alerts.

The result was we saved them 20% on their overall costs.

How Our Unique Framework Solves Performance Engineering Challenges

Perform’s performance engineering framework was designed to address the challenges mentioned above. Our approach includes:

  • Early performance testing in the development lifecycle
  • Integration of artificial intelligence and machine learning into our testing approach
  • Testing of microservices and containers as a part of the entire system
  • Cloud-based testing to simulate real-world scenarios

Performance Testing and Quality Assurance Development​

We offer a range of services to help your business with performance testing and quality assurance development. These include:

  • Center of Excellence (COE) development
  • Performance testing services
  • Quality assurance consulting
  • Project management and delivery
  • Staff augmentation


As we continue into 2024, we encourage you to prioritize performance engineering to ensure an excellent user experience.

Whether it’s the holiday season ahead or if you have a big product launch with a heavy influx of traffic, Performance has developed a framework to solve the kind of challenges you’re likely to face.

If you’d like to consult with us about your performance testing and quality assurance development needs, connect with us today.





“Total Performance Consulting helped MHE build out new teams, supplement existing teams, and improve our overall performance testing posture”.


Sr. Director, Application Performance and Development Operations, McGraw Hill Education

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